Differential display (DD) is one of the most commonly used approaches for identifying differentially expressed genes. Despite the great impact of the method on biomedical research, there has been a lack of automation of DD technology to increase its throughput and accuracy for a systematic gene expression analysis. Most of previous DD work has taken a "shot-gun" approach of identifying one gene at a time, with a limited PCR reactions set up manually, giving DD a low-tech and low-throughput image. With our newly solved DD mathematical model, which has been validated by computer simulations, global analysis of gene expression by DD technology is no longer a shot in the dark. After identifying the "rate-limiting" factors that contribute to the "noise" level of DD method, we have optimized the DD process with a new platform that incorporates fluorescent digital readout and automated liquid handling. The resulting streamlined fluorescent DD (FDD) technology offers an unprecedented accuracy, sensitivity and throughput in comprehensive and quantitative analysis of gene expression. We plan to apply this newly integrated FDD technology to conduct a systematic and comprehensive screening for p53 tumor-suppressor gene targets using two well-defined biological systems which features tetracycline regulated expression of wild-type p53 in both colon cancer and lung cancer cells that undergo rapid apoptosis upon p53 induction. The p53 target genes identified will be subjected to secondary screening processes, including the use of methods independent of FDD, and an additional cell system where endogenous p53 can be activated by DNA damaging agents. In the final phase of this study, three other technologies, namely, the inducible enhanced green fluorescence protein (EGFP) co-expression system, in-frame GFP fusion expression system and mammalian RNA interference (RNAi), will be incorporated to provide functional identification and sub-cellular localization of p53 target genes involved in apoptosis. We anticipate that this systematic and pioneering study will not only uncover many (if not all) additional target genes of the most important tumor-suppressor gene, but also will provide an experimental basis for an objective comparison of major technologies for analysis of differential gene expression, in terms of accuracy, comprehensiveness and throughput. Such a cross-platform comparison in studying the same biological system will be crucial in pinpointing the strength and weakness of each method and helpful for future improvement of the next generation technologies through complementation, integration and refinement.